Baselines.
We compare with the strong state-of-the-art baselines, namely TENT [1]}, BN [2]}, and Prediction Time Normalization (PTN) [3]}. All methods are run in the SITA setting, that is, the test batch size for all experiments is fixed to 1 and no method is allowed to run an online optimization or to maintain online statistics. For TENT, we use one epoch of optimization with a learning rate of \(10^{-3}\)  (this is the best result we obtained by experimenting over a range between \([10^{-2}, 10^{-5}]\) ) with Adam optimizer as it provides the best computation time vs performance trade-off. For BN, we showcase their method with the recommended hyper-parameter setting (\(N=16\) ) for the single image case.
